Histogram equalization

About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.

Multidimensional Contrast Limited Adaptive Histogram Equalization

Abstract: Contrast enhancement is an important preprocessing technique for improving the performance of downstream tasks in image processing and computer vision. Among the existing approaches based on nonlinear histogram transformations, contrast limited adaptive histogram equalization (CLAHE) is a popular choice for dealing with 2D images obtained in natural and scientific settings. The recent hardware upgrade in data acquisition systems results in significant increase in data complexity, including their sizes and dimensions. Measurements of densely sampled data higher than three dimensions, usually composed of 3D data as a function of external parameters, are becoming commonplace in various applications in the natural sciences and engineering. The initial understanding of these complex multidimensional datasets often requires human intervention through visual examination, which may be hampered by the varying levels of contrast permeating through the dimensions. We show both qualitatively and quantitatively that using our multidimensional extension of CLAHE (MCLAHE) simultaneously on all dimensions of the datasets allows better visualization and discernment of multidimensional image features, as demonstrated using cases from 4D photoemission spectroscopy and fluorescence microscopy. Our implementation of multidimensional CLAHE in Tensorflow is publicly accessible and supports parallelization with multiple CPUs and various other hardware accelerators, including GPUs.

Method and apparatus for estimating feature values in a region of a sequence of images

Abstract: A method and apparatus are disclosed that estimate the brightness or other feature values of unchanging or slowly changing regions of an image in a sequence of video images even when the regions is obscured by objects over large portions of the video sequence. The apparatus and method generate a histogram for each image region position over a plurality of image frames in the sequence. The mode, or most frequently occurring value, of the image region as indicated by the histogram is selected as representing the unchanging portion of the image. The mode values of all of the regions are then assembled to form a composite image of the unchanging or slowly changing feature values. According to one method, the histogram is generated using a recursive filter. In order to process images that exhibit some motion from frame to frame, the images in the video sequence may be aligned before generating the histogram. If the camera produces artifacts such as variations in the image caused by an automatic gain control (AGC) function, each image in the sequence of video images may be filtered either temporally or spatially before performing the histogramming operation to remove these artifacts. To reduce processing time, the image processing may be spaced in time such that only every nth image is processed. Alternatively, each region of an image sequence may be processed at random irregular intervals in order to obtain the histogram. In one embodiment of the invention, the histogram is applied over relatively small groups of frames in order to generate a noise reduced image.

Expanding histogram of colors with gridding to improve tracking accuracy

Abstract: Using color information in object tracking is a prudent choice, but the vast variety of choices and difficulties of obtaining a desirable stable result, unnerves many scholars. Color histograms, as a compact and robust representation is the center of attention while it suffers from lack of spatial information about colors. Besides, comparison and updating such histograms in a meaningful and efficient manner is challenging. In this paper, we proposed the idea of gridding for color histogram, which grants specific statistical property to the histogram through a decomposition phase followed by a recombination stage. Additionally, a thorough comparison of the modern similarity functions and model update techniques in RGB colorspace is presented. This comparison reveals that our proposed method in combination with established similarity measures, enhances the tracking performance.

Power-constrained contrast enhancement for OLED displays based on histogram equalization

Abstract: A novel power-constrained contrast enhancement algorithm for organic light-emitting diode (OLED) displays is proposed in this work. We first develop the log-modified histogram equalization (LMHE) scheme, which reduces overstretching artifacts of the conventional histogram equalization technique. Then, we model the power consumption in OLED displays, and incorporate it into LMHE to achieve the optimal tradeoff between contrast enhancement and power saving. Simulation results demonstrate that the proposed algorithm can reduce the power consumption significantly, while preserving image qualities.

Low-light image restoration using bright channel prior-based variational Retinex model

Abstract: This paper presents a low-light image restoration method based on the variational Retinex model using the bright channel prior (BCP) and total-variation minimization. The proposed method first estimates the bright channel to control the amount of brightness enhancement. Next, the variational Retinex-based energy function is iteratively minimized to estimate the improved illumination and reflectance using the BCP. Contrast of the estimated illumination is enhanced using the gamma correction and histogram equalization to reduce a color distortion and noise amplification. Experimental results show that the proposed method can provide the better restored result than the existing methods without unnatural artifacts such as noise amplification and halo effects near edges.